One Customer, Many Records: 5-Step Framework to Fix Duplicate Data at Scale

You’ve cleaned your data before. Probably more than once.
But here you are again with the same problem, only ten times worse: thousands of duplicate customer records with multiple versions of contact data scattered across CRM systems, CSV files, and ERP systems causing a serious operational bottleneck.

You may have also spent weeks, if not months, trying to fix the issue systematically, only to find yourself stuck in a cycle of spreadsheets, workarounds, and wasted manhours. A recent Experian report claims teams spend around 3.1 hours/week manually cleaning data, and 55% of businesses say poor-quality data results in wasted resources and lost productivity.

We know how problematic this can get.

And this is why we’ve built this framework to help you clean and match customer contact data within minutes using a 5-step process that is based on years of working with inconsistent datasets across industries.

Why Do You Need a Framework?

When data managers start fixing duplicate data, they often dive straight into the raw data, exporting files, using VLOOKUP or IF statements in Excel, or running manual deduplication queries in SQL. This is a manual process that is error-prone and can take weeks of execution time.

It’s a widely acknowledged truth that data professionals spend up to 60% of their time on finding, preparing and cleaning data, often without a systematic framework.

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This framework is designed to help you reclaim that time and improve your data matching accuracy for cleaning and deduplicating CRM records, preparing data for a migration, or for internal reporting. It combines a structured, repeatable approach with practical guidance on how to use WinPure’s Clean & Match software for fast, high-confidence results even on messy or incomplete datasets.

Ready? Let’s dive in.

Step 1: Identify Critical Data Quality Issues in the Project

If you’ve been tasked with a time-sensitive migration audit, a CRM data cleanup, or deduping customer records from the last few years, you’re probably feeling the pressure to fix everything at once – across systems, teams, and formats.

You might be tempted to run a quick deduplication of name fields as a good starting point, but when you have 20,000 duplicates stored across multiple systems, this approach leads to more complexity, and a lot of stressful work!

Here’s what we recommend as a starting point to cleaning duplicate customer records:

Start by narrowing your scope. For example, choose only Customer records from CRM deals created in the last 12 months instead of tackling all-time customer data. This lets you:

• Work with a manageable and current dataset
• Test your match logic for customer data in a focused environment
• Avoid edge cases and reduce error risk
• Build confidence with a clear before-and-after view

If you’re unsure where to start, WinPure’s data profiling tool can help you identify which datasets have the highest duplicate record density, missing field rates, quality gaps, or inconsistencies — so you know exactly what needs your attention first.

Step 2: Building a Tiered Match Strategy for Handling Duplicates

One of the most common points of failure in customer data matching is when unique identifier fields (like emails and phone numbers) have inconsistencies like spelling differences, formatting issues, abbreviations, and incomplete strings that cannot be resolved using traditional exact-match systems.

While SQL for data deduplication is powerful, it falls short when you’re dealing with similar but not identical records such as, Johnny Smith” vs. “John Smith”, “ABC Inc.” vs. “A.B.C. Incorporated”. Most SQL engines don’t support fuzzy logic natively, and even with extensions, performance and scalability can quickly become limiting.

That’s why using fuzzy matching for contact data is critical when you’re dealing with inconsistent or fragmented records.

With WinPure’s fuzzy matching engine, you can compare fields like names, addresses, or company names based on similarity scores rather than exact values. Moreover, with our AI entity resolution capabilities you can resolve complex duplicates at an entity level where it takes into consideration the “context” of a data field and surfaces duplicates that goes beyond basic name and contact records.

Here’s a quick overview on the difference between fuzzy match and AI match.

To improve both accuracy and control, we recommend a tiered match strategy that blends fuzzy logic with field prioritization:

§  Tier 1: High-confidence fields – email, customer ID, phone number

§  Tier 2: Contextual fields – company name, postal code, source system

§  Tier 3: Tie-breakers – session IDs, region, or recent activity

A solid match rule might involve fuzzy matching on Company Name, while combining it with exact matches on Postal Code and Session ID Date. This reduces false positives while giving you flexibility when fields are messy or incomplete.

Step 3: Connect with Business Users to Verify Decisions

Once you’ve defined which fields you’ll use to identify duplicates, the next step is to validate your data matching logic with business stakeholders, meaning the people who use the records every day.

What looks right in your matching rules may not align with how other departments interpret the data. For example, a simple challenge as a shared email address may signal a duplicate data challenge to IT or data teams, but to marketing, they might be a valid entry from a webinar series where multiple attendees registered under a single domain-managed account.

This alignment step is crucial when you’re dealing with customer data across multiple systems, where field usage, naming conventions, or even data quality may vary widely.

Some quick questions to help you get started:

• Are there shared identifiers (emails, domains, phone numbers) that are legitimate in this context?
• Are any fields used differently across teams (e.g., “Account Name” being reused internally)?
• Are there known exceptions or legacy formats that could trigger false matches?
• Should any records be excluded from merging due to contractual or compliance reasons?

Validating this early avoids rework, missed context, and unnecessary tension between teams later on.

Step 4: Making a decsion on data handling

At this point, most teams would rush into cleaning up the data, only to realize later that they’ve missed a key decision-making factor.

You’ll need to define how you’ll resolve duplicates once they are matched:

• Which record becomes the “master”? Will you prioritize recency, completeness, or data source?
• How will you handle field-level conflicts? For example, if two matched records have different phone numbers, do you keep the latest, both, or flag for manual review?
• What do you want to exclude from merging? There may be edge cases — like partner accounts or intentionally duplicated contacts — that should be left untouched.

Also consider:

• Whether your changes will impact any live workflows or reports
• Who needs to be informed before the merge
• Will you need an audit trail or rollback option in case of errors?

Cleaning the data is where errors become visible. A little planning here protects the credibility of the entire process — and ensures you don’t spend more time fixing fixes.

Step 5: Resolve in batches and review as you go

Even if your match logic feels airtight, avoid merging all records in one go. Instead, apply a batch deduplication strategy.

Start with a small subset of high-confidence matches which are records that meet all key criteria without conflicting fields. Run your merge or dedupe process, review the output, and check for:

• Unexpected merges or false positives
• Loss of important fields or overwrites
• Any impact on linked systems or workflows

Maintain a simple log of what’s been processed, which logic was applied, how many records were changed, and what was flagged for review. This becomes essential if you need to answer questions later or replicate the process for other datasets.

If you’re using a tool like WinPure, most of this workflow — from match scoring to merge previews is already built in.

How to Make this Framework Work for Your Data

You don’t need to wait for a system overhaul or a six-week sprint to start fixing duplicate records. This framework is designed to be fast, repeatable, and scalable.

Start by picking one dataset tied to a current initiative (like CRM cleanup or migration), and follow the 5 steps to structure your match rules, involve stakeholders, and clean with confidence.

If you’re using WinPure, the fuzzy matching and preview features will speed up the process — but the real value lies in the structure. You’ll know exactly what to match, how to review it, and how to scale the fix.

To help you implement this process efficiently, we’ve created a Data Matching Checklist that includes:

• Fuzzy match setup guidance
• Merge and review rules
• Deduplication management rules
• And space to log your batch review progress

Whether you’re doing a one-off cleanup or designing a repeatable process, this framework and workbook will keep your team aligned and your data quality challenges under control.